Indoor tracking and navigation is a fundamental need for pervasive and context-aware smartphone applications. Applications of indoor positioning include smart retail, navigation through large public spaces like transport hubs, and assisted living. The ultimate objective of indoor positioning systems is to provide continuous, reliable and accurate positioning on smartphone class devices. Inertial tracking, or “dead reckoning”, with or without the aid of existing maps, has received a lot of attention recently, but existing solutions lack generality and robustness. They are typically targeted at achieving high accuracy in specific contexts, and are fine tuned for specific phone placements (i.e. positions on the user's body, etc.), users, devices or environments. When tested in different conditions from the ones designed for, their performance degrades significantly. For example, the majority of existing solutions assume that the user holds the device in a specific way (e.g. texting mode) [1] (see list of references below). More advanced solutions first use a classifier to infer the user walking pattern and phone placement (e.g. hand swinging, texting, etc.) [2], [3], and then exploit this information to optimize the inertial tracking system [4]. However, even the state of the art approaches can only handle a limited number of phone placement options. When the user deviates from this predefined set, the tracking system is at a loss for how to handle the new case. Another major issue is that the inertial tracking system typically assumes knowledge of a number of parameters that account for user, device and environment variability. Examples of user variability parameters include those that relate the user height, step frequency and acceleration variance, to their step length [5]-[8]. Further parameters are needed to model the noise of inertial sensors, and environment-specific magnetic distortions [9]. The vast tuning effort involved in optimizing pedestrian dead reckoning (PDR) systems is one of the major hurdles that prevent inertial tracking systems from becoming mainstream.
In the present invention a move away from context-specific fine-tuning is made, to more general-purpose robust tracking. Another aspect is lifelong learning of the system.
Herein reference is made to the following publications:    [1] Z. Xiao, H. Wen, A. Markham, and N. Trigoni, “Lightweight map matching for indoor localization using conditional random fields (BEST PAPER),” in The International Conference on Information Processing in Sensor Networks (IPSN'14), (Berlin, Germany), 2014.    [2] J. Yang, “Toward physical activity diary: motion recognition using simple acceleration features with mobile phones,” in Proc. 1st Int. workshop Interactive multimedia for consumer electronics, (Beijing, China), pp. 1-9, 2009.    [3] J. S. Wang, C. W. Lin, Y. T. Yang, and Y. J. Ho, “Walking pattern classification and walking distance estimation algorithms using gait phase information,” IEEE Trans. Biomedical Engineer., vol. 59, no. 10, pp. 2884-2892, 2012.    [4] N. Roy, H. Wang, and R. R. Choudhury, “I am a smartphone and i can tell my users walking direction,” in Mobisys, 2014.    [5] L. Fang, P. Antsaklis, L. Montestruque, M. B. McMickell, M. Lemmon, Y. Sun, and H. Fang, “Design of a wireless assisted pedestrian dead reckoning system—the NavMote experience,” Instrumentation and . . . , vol. 54, no. 6, pp. 2342-2358, 2005.    [6] S. H. Shin, C. G. Park, and J. W. Kim, “Adaptive step length estimation algorithm using low-cost MEMS inertial sensors,” in Proc. IEEE Sensors App. Symp., no. February, (San Diege, Calif., USA), pp. 1-5, 2007.    [7] I. Bylemans, M. Weyn, and M. Klepal, “Mobile Phone-Based Displacement Estimation for Opportunistic Localisation Systems,” in Proc. 3rd Int. Conf. Mob. Ubi. Comput. Syst. Services Technol. (UBICOMM'09), (Sliema), pp. 113-118, leee, Oct. 2009. 29    [8] V. Renaudin, M. Susi, and G. Lachapelle, “Step length estimation using handheld inertial sensors,” Sensors, vol. 12, pp. 8507-8525, January 2012.    [9] J. Chung, M. Donahoe, C. Schmandt, and I.-J. Kim, “Indoor location sensing using geo-magnetism,” in Mobisys, 2011.    [10] M. Susi, V. Renaudin, and G. Lachapelle, “Motion mode recognition and step detection algorithms for mobile phone users.,” Sensors, vol. 13, pp. 1539-62, January 2013.    [11] Y. S. Suh, “Orientation Estimation Using a Quaternion-Based Indirect Kalman Filter With Adaptive Estimation of External Acceleration,” IEEE Trans. Instrument. Measurement, vol. 59, pp. 3296-3305, December 2010.    [12] X. Yun and E. R. Bachmann, “Design, Implementation, and Experimental Results of a Quaternion-Based Kalman Filter for Human Body Motion Tracking,” IEEE Trans. Robotics, vol. 22, pp. 1216-1227, December 2006.    [13] B. Huyghe, J. Doutreloigne, and J. Vanfleteren, “3D orientation tracking based on unscented Kalman filtering of accelerometer and magnetometer data,” in Proc. IEEE Sens. App. Symp. (SAS'09), (New Orleans, La., USA), pp. 1-5, 2009.    [14] T. Harada, T. Mori, and T. Sato, “Development of a Tiny Orientation Estimation Device to Operate under Motion and Magnetic Disturbance,” The Int. J. Robotics Res., vol. 26, pp. 547-559, June 2007.    [15] D. Mizell, “Using gravity to estimate accelerometer orientation,” in Proc. 7th IEEE Int. Symp. Wearable Computers (ISWC'03), pp. 252-253, leee, 2003.    [16] H. Lu, J. Yang, Z. Liu, N. D. Lane, T. Choudhury, and A. T. Campbell, “The Jigsaw continuous sensing engine for mobile phone applications,” in Proc. 8th ACM Conf. Embedded Netw. Sensor Syst. (SenSys'10), (New York, N.Y., USA), pp. 71-84, ACM Press, 2010.    [17] S. Hemminki, P. Nurmi, and S. Tarkoma, “Accelerometer-based transportation mode detection on smartphones,” in Proc. 11th ACM Conf. Embedded Netw. Sensor Syst. (Sensys'13), (New York, N.Y., USA), pp. 1-14, 2013.    [18] J. Rose and J. G. Gamble, Human Walking. Baltimore, Pa., USA: Lippincott, Williams and Wilkins, 3rd ed., 2006.    [19] J. W. Kim, H. J. Jang, D. H. Hwang, and C. Park, “A step, stride and heading determination for the pedestrian navigation system,” Journal of Global Position. Syst., vol. 3, no. 1, pp. 273-279, 2004.    [20] S. Beauregard and H. Haas, “Pedestrian dead reckoning: A basis for personal positioning,” in Proc. 3rd Workshop Pos. Nay. Commun. (WPNC'06), pp. 27-36, 2006.    [21] S. H. Shin, M. S. Lee, and P. C. G., “Pedestrian dead reckoning system with phone location awareness algorithm,” in Proc. IEEE/ION Position Location Nay. Symp. (PLANS'10), pp. 97-101, 2010.    [22] P. Goyal, V. J. Ribeiro, H. Saran, and A. Kumar, “Strap-down Pedestrian Dead-Reckoning system,” in Proc. Int. Conf. Indoor Pos. Indoor Nay. (IPIN'11), pp. 1-7, leee, September 2011.    [23] N. Ravi, N. Dandekar, P. Mysore, and M. Littman, “Activity recognition from accelerometer data,” in Proc. 7th Conf. Innov. App. Artificial Intell. (AAAI'05), pp. 1541-1546, 2005.    [24] J.-g. Park, A. Patel, D. Curtis, S. Teller, and J. Ledlie, “Online pose classification and walking speed estimation using handheld devices,” in Proc. ACM Conf. Ubi. Comput. (UbiComp'12), (New York, N.Y., USA), pp. 1-10, ACM Press, 2012.    [25] A. Brajdic and R. Harle, “Walk detection and step counting on unconstrained smartphones,” in Proc. ACM Conf. Ubi. Comput. (UbiComp'13), (Zurich, Switzerland), pp. 225-234, ACM Press, 2013. 30    [26] H. Ying, C. Silex, and A. Schnitzer, “Automatic step detection in the accelerometer signal,” in Proc. 4th Int. Workshop Wearable and Implantable Body Sensor Netw. (BSN'07), pp. 80-85, 2007.    [27] A. Rai and K. Chintalapudi, “Zee: Zero-effort crowdsourcing for indoor localization,” in Proc. 18th Ann. Int. Conf. Mob. Comput. Netw. (MobiCom'12), (Istanbul, Turkey), pp. 1-12, 2012.    [28] M. Alzantot and M. Youssef, “UPTIME: Ubiquitous pedestrian tracking using mobile phones,” in Proc. IEEE Wirel. Commun. Netw. Conf. (WCNC'12), pp. 3204-3209, leee, April 2012.    [29] R. Harle, “A Survey of Indoor Inertial Positioning Systems for Pedestrians,” IEEE Commun. Surveys & Tutorials, vol. 15, pp. 1281-1293, January 2013.    [30] P. Barralon, N. Vuillerme, and N. Noury, “Walk detection with a kinematic sensor: frequency and wavelet comparison,” in Proc. Ann. Int. Conf. IEEE Eng. Med. Bio. Soc., vol. 1, pp. 1711-4, January 2006.    [31] M. N. Nyan, F. E. H. Tay, K. H. W. Seah, and Y. Y. Sitoh, “Classification of gait patterns in the time-frequency domain,” J. Biomech, vol. 39, pp. 2647-56, January 2006.    [32] E. Foxlin, “Pedestrian tracking with shoe-mounted inertial sensors,” IEEE Comput. Graph. App., vol. 25, no. 6, pp. 38-46, 2005.    [33] R. Faragher and R. Harle, “SmartSLAMan efficient smartphone indoor positioning system exploiting machine learning and opportunistic sensing,” in Proc. 26th Int. Tech. Meet. Satel. Div. Institute of Nay. (ION GNSS+'13), pp. 1-14, 2013.    [34] W. Chen, R. Chen, Y. Chen, H. Kuusniemi, and J. Wang, “An effective Pedestrian Dead Reckoning algorithm using a unified heading error model,” in Proc. IEEE/ION Position, Location and Nay. Symp, pp. 340-347, leee, May 2010.    [35] V. Renaudin, M. Susi, and G. Lachapelle, “Step length estimation using handheld inertial sensors,” Sensors, vol. 12, no. 7, pp. 8507-8525, 2012.    [36] F. Li, C. Zhao, G. Ding, J. Gong, C. Liu, and F. Zhao, “A reliable and accurate indoor localization method using phone inertial sensors,” in Proc. ACM Conf. Ubi. Comput. (UbiComp'12), (New York, N.Y., USA), p. 421, ACM Press, 2012.    [37] J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, and A. Aggarwal, “Efficient, generalized indoor WiFi GraphSLAM,” in Proc. IEEE Int. Conf. Robot. Automat., (Shanghai, China), pp. 1038-1043, leee, May 2011.    [38] M. H. Afzal, V. Renaudin, and G. Lachapelle, “Assessment of indoor magnetic field anomalies using multiple magnetometers,” in Proc. 23th Int. Tech. Meet. Satel. Div. Institute of Nay. (ION GNSS+'10), no. September, pp. 21-24, 2010.    [39] H. Trinh, A machine learning approach to recovery of scene geometry from images. Ph.d thesis, Toyota Technological Institute at Chicago, 2010.